Minimum Change ≠ Best Cleaning: Parallel and Incremental Error Detection under Integrity Constraints
Summary: Shows minimum-change repairs fail when errors co-occur; uses Bayesian inference to score conflicting cells and pinpoint erroneous attribute values instead of minimal edits. Introduces provably scalable parallel conflict detection and incremental, parallel error-detection algorithms. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Jiahui Chen (Nankai University)
- 2. Yu Sun (Nankai University)
- 3. Shaoxu Song (Tsinghua University)
- 4. Haiwei Zhang (Nankai University)
- 5. Xiaojie Yuan (Nankai University)
BibTeX Citation
@inproceedings{chen_sigmod26,
title = {{Minimum Change ≠ Best Cleaning: Parallel and Incremental Error Detection under Integrity Constraints}},
author = {Chen, Jiahui and Sun, Yu and Song, Shaoxu and Zhang, Haiwei and Yuan, Xiaojie},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3749174},
url = {https://dl.acm.org/doi/10.1145/3749174},
year = {2026}
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